Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import joblib
+from tensorflow.keras.models import load_model
+import requests
+import os
+
+# URLs to your saved models and scaler
+scaler_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/scaler_X.pkl'
+rf_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/rf_model.pkl'
+mlp_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/mlp_model.h5'
+meta_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/meta_model.pkl'
+
+# Paths to save the downloaded models
+scaler_path = 'scaler_X.pkl'
+rf_model_path = 'rf_model.pkl'
+mlp_model_path = 'mlp_model.h5'
+meta_model_path = 'meta_model.pkl'
+
+def download_model(url, path):
+    if not os.path.exists(path):
+        print(f"Downloading {path}...")
+        response = requests.get(url)
+        with open(path, 'wb') as f:
+            f.write(response.content)
+        print(f"Downloaded {path}.")
+    else:
+        print(f"{path} already exists.")
+
+# Download the models and scaler if they don't exist locally
+download_model(scaler_url, scaler_path)
+download_model(rf_model_url, rf_model_path)
+download_model(mlp_model_url, mlp_model_path)
+download_model(meta_model_url, meta_model_path)
+
+try:
+    # Load the scaler and models
+    scaler_X = joblib.load(scaler_path)
+    loaded_rf_model = joblib.load(rf_model_path)
+    loaded_mlp_model = load_model(mlp_model_path)
+    loaded_meta_model = joblib.load(meta_model_path)
+    print("Models and scaler loaded successfully.")
+except Exception as e:
+    print(f"Error loading models or scaler: {e}")
+
+def predict_new_values(new_input_data):
+    try:
+        # Ensure the new input data is in the correct format
+        print(f"Raw Input Data: {new_input_data}")
+        new_input_data = np.array(new_input_data).reshape(1, -1)
+        
+        # Scale the new input data
+        new_input_scaled = scaler_X.transform(new_input_data)
+        print(f"Scaled Input Data: {new_input_scaled}")
+        
+        # Make predictions with both base models
+        mlp_predictions_new = loaded_mlp_model.predict(new_input_scaled)
+        rf_predictions_new = loaded_rf_model.predict(new_input_scaled)
+        
+        # Combine the predictions
+        combined_features_new = np.concatenate([mlp_predictions_new, rf_predictions_new], axis=1)
+        print(f"Combined Features: {combined_features_new}")
+        
+        # Use the loaded meta model to make predictions on the new data
+        loaded_meta_predictions_new = loaded_meta_model.predict(combined_features_new)
+        print(f"Meta Model Predictions: {loaded_meta_predictions_new}")
+        
+        return loaded_meta_predictions_new[0]
+    except Exception as e:
+        print(f"Error in prediction: {e}")
+        return ["Error", "Error", "Error", "Error", "Error", "Error"]
+
+def gradio_interface(velocity, temperature, precipitation, humidity):
+    try:
+        input_data = [velocity, temperature, precipitation, humidity]
+        print(f"Input Data: {input_data}")
+        predictions = predict_new_values(input_data)
+        print(f"Predictions: {predictions}")
+        return [
+            f"{predictions[0] * 100:.2f}%" if predictions[0] != "Error" else "Error",
+            f"{predictions[1] * 100:.2f}%" if predictions[1] != "Error" else "Error",
+            f"{predictions[2] * 100:.2f}%" if predictions[2] != "Error" else "Error",
+            f"{predictions[3] * 100:.2f}%" if predictions[3] != "Error" else "Error",
+            f"{predictions[4] * 100:.2f}%" if predictions[4] != "Error" else "Error",
+            f"{predictions[5] * 100:.2f}%" if predictions[5] != "Error" else "Error"
+        ]
+    except Exception as e:
+        print(f"Error in Gradio interface: {e}")
+        return ["Error", "Error", "Error", "Error", "Error", "Error"]
+
+inputs = [
+    gr.Slider(minimum=0, maximum=100, value=50,step=0.5, label="Velocity (mph)"),
+    gr.Slider(minimum=-30, maximum=50, value=0,step=0.5, label="Temperature (°C)"),
+    gr.Slider(minimum=0, maximum=10, value=0,step=0.01, label="Precipitation (inch)"),
+    gr.Slider(minimum=0, maximum=100, value=50, label="Humidity (%)")
+]
+
+outputs = [
+    gr.Textbox(label="Front Left"),
+    gr.Textbox(label="Front Right"),
+    gr.Textbox(label="Left"),
+    gr.Textbox(label="Right"),
+    gr.Textbox(label="Roof"),
+    gr.Textbox(label="Rear")
+]
+
+gr.Interface(
+    fn=gradio_interface, 
+    inputs=inputs, 
+    outputs=outputs, 
+    title="Environmental Factor-Based Contamination Level Prediction",
+    description="Enter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs."
+).launch()